Developing a bone mineral density test result letter to send to patients: a mixed-methods study
Bibliographic record
Abstract
PURPOSE: To use a mixed-methods approach to develop a letter that can be used to notify patients of their bone mineral density (BMD) results by mail that may activate patients in their bone-related health care. PATIENTS AND METHODS: A multidisciplinary team developed three versions of a letter for reporting BMD results to patients. Trained interviewers presented these letters in a random order to a convenience sample of adults, aged 50 years and older, at two different health care systems. We conducted structured interviews to examine the respondents' preferences and comprehension among the various letters. RESULTS: A total of 142 participants completed the interview. A majority of the participants were female (64.1%) and white (76.1%). A plurality of the participants identified a specific version of the three letters as both their preferred version (45.2%; P<0.001) and as the easiest to understand (44.6%; P<0.01). A majority of participants preferred that the letters include specific next steps for improving their bone health. CONCLUSION: Using a mixed-methods approach, we were able to develop and optimize a printed letter for communicating a complex test result (BMD) to patients. Our results may offer guidance to clinicians, administrators, and researchers who are looking for guidance on how to communicate complex health information to patients in writing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".